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Wednesday, 26 September 2018

Types of inference engines in Artificial Intelligence

As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:

Truth Maintenance.

Hypothetical Reasoning.

Fuzzy Logic.

Ontology Classification.

Truth Maintenance

Truth maintenance systems record the dependencies in a knowledge-base so that when facts are altered dependent knowledge can be altered accordingly.

For example, if the system learns that Jon is no longer known to be a man it will revoke the assertion that Jon is mortal.

Hypothetical Reasoning

In hypothetical reasoning, the knowledge base can be divided up into many possible views, aka worlds.

This allows the inference engine to explore multiple possibilities in parallel.

In this simple example, the system may want to explore the consequences of both assertions, what will be true if Jon is a Man and what will be true if he is not?

Fuzzy Logic

Once of the first extensions of simply using rules to represent knowledge was also to associate a probability with each rule.

So, not to assert that Jon is mortal but to assert Jon may be mortal with some probability value.

Simple probabilities were extended in some systems with sophisticated mechanisms for uncertain reasoning and combination of probabilities.

Ontology Classification

With the addition of object classes to the knowledge base a new type of reasoning was possible. Rather than reason simply about the values of the objects the system could also reason about the structure of the objects as well.

In this simple example Man can represent an object class and R1 can be defined as a rule that defines the class of all men.

These types of special purpose inference engines are known as classifiers.Although they were not highly used in expert systems classifiers are very powerful for unstructured volatile domains and are a key technology for the Internet and the emerging.